(1) Categorical Depth Distributions. In order to perform 3D detection, we predict pixel-wise categorical depth distributions to accurately locate image information in 3D space. Each predicted distribution describes the probabilities that a pixel belongs to a set of predefined depth bins. We encourage our distributions to be as sharp as possible around the correct depth bins, in order to encourage our network to focus more on image information where depth estimation is both accurate and confident [1]}. By doing so, our network is able to produce sharper and more accurate features that are useful for 3D detection (see Figure REF ). On the other hand, our network retains the ability to produce less sharp distributions when depth estimation confidence is low. Using categorical distributions allows our feature encoding to capture the inherent depth estimation uncertainty to reduce the impact of erroneous depth estimates, a property shown to be key to CaDDN's improved performance in Section REF . Sharpness in our predicted depth distributions is encouraged through supervision with one-hot encodings of the correct depth bin, which can be generated by projecting LiDAR depth data into the camera frame.
